Papers › Unsupervised Part Segmentation through Disentangling Appearance and Shape
Unsupervised Part Segmentation through Disentangling Appearance and Shape
Shilong Liu, Lei Zhang, Xiao Yang, Hang Su, Jun Zhu
We study the problem of unsupervised discovery and segmentation of object parts, which, as an intermediate local representation, are capable of finding intrinsic object structure and providing more explainable recognition results. Recent unsupervised methods have greatly relaxed the dependency on annotated data which are costly to obtain, but still rely on additional information such as object segmentation mask or saliency map. To remove such a dependency and further improve the part segmentation performance, we develop a novel approach by disentangling the appearance and shape representations of object parts followed with reconstruction losses without using additional object mask information. To avoid degenerated solutions, a bottleneck block is designed to squeeze and expand the appearance representation, leading to a more effective disentanglement between geometry and appearance. Combined with a self-supervised part classification loss and an improved geometry concentration constraint, we can segment more consistent parts with semantic meanings. Comprehensive experiments on a wide variety of objects such as face, bird, and PASCAL VOC objects demonstrate the effectiveness of the proposed method.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
No code repository is listed for this paper in the archive or in Syntology's graph.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Unsupervised Facial Landmark Detection | AFLW Unaligned | UPSDAP | NME | 13.13 | #1 of 4 | Archive leaderboard | report |
| Unsupervised Facial Landmark Detection | AFLW Unaligned | IMM | NME | 13.31 | #2 of 4 | Archive leaderboard | report |
| Unsupervised Facial Landmark Detection | AFLW Unaligned | Lorenz2019unsupervised | NME | 13.6 | #3 of 4 | Archive leaderboard | report |
| Unsupervised Facial Landmark Detection | AFLW Unaligned | SCOPS | NME | 16.05 | #4 of 4 | Archive leaderboard | report |
| Unsupervised Facial Landmark Detection | MAFL Unaligned | UPSDAS | NME | 12.26 | #5 of 9 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections